code: def __init__(
      self,
      num_items,
      item_embed,
      item_hidden_size,
      item_max_length,
      item_num_heads,
      item_num_layers,
      item_transformer_dropout,
      item_pooling_dropout,
      cross_size,
      prefix=None,
      params=None,
      **kwargs,
  ):
      super(SequenceTransformer, self).__init__(**kwargs)
      # super().__init__(prefix=prefix, params=params)
      with self.name_scope():
          self.item_pooling_dp = MeanMaxPooling(
              dropout=item_pooling_dropout
          )
          self.item_encoder = TransformerEncoder(
              units=item_embed,
              hidden_size=item_hidden_size,
              num_heads=item_num_heads,
              num_layers=item_num_layers,
              max_length=item_max_length,
              dropout=item_transformer_dropout,
          )
          self.embedding = gluon.nn.Embedding(
              input_dim=num_items, output_dim=item_embed
          )
          self.dense = gluon.nn.Dense(cross_size)
  
file_path: analytics-zoo/pyzoo/zoo/models/recommendation/txt.py
imports: from mxnet import gluon
  from gluonnlp.model.transformer import TransformerEncoder, TransformerEncoderCell
label_desc: Use image node_id 1 to create a new SequenceTransformer object from inherited base classes: gluon.nn with example: obj = SequenceTransformer(num_items, item_embed, item_hidden_size, item_max_length, item_num_heads, item_num_layers, item_transformer_dropout, item_pooling_dropout, cross_size, prefix, params)